Exercise and activity devices that measure biometric and environmental data for field team sports such as heart rate, speed, stride rate, altitude, temperature, power, distance per stride, location, acceleration, direction the user is facing, distance, time and other parameters currently exist. This data is displayed on a watch or device screen, or logged on a data recorder.
There are also video logging systems where occurrences within a recorded game can be identified and labelled manually by an expert coach for later use in demonstrating various situations to players.
The state of the art has developed further where multiple video cameras can be pointed at a playing field for soccer or a court for basketball as examples and algorithms can triangulate the location of each player and the ball on the field many times a second for an entire game.
Still further advancements have occurred where data on the location and other physical, physiological and environmental data which can also be transmitted from each player to a base station on the side of the field in real time.
There are now also sensors in the ball that can relay data to a base station or to a user's watch during play.
The difficulty is that these systems generate hundreds of thousands of data points per player per game. For example, location sensing camera systems generate location data at 25 frames per second; 135,000 data points for x axis and 135,000 y axis data points to locate a player for a 90 minute soccer match. GPS used in practice for team sports generate 10,800 latitude and longitude data points during a practice per player. For an 11 man team that is 2,970,000 and 118,800 data points respectively per team per game. Analysing a season of the English Premier League consisting of 38 games amounts to 112,860,000 data points if no substitutes are used. Analysis and processing of this data is very time consuming and very difficult to use in a real time situation.
Faced with this amount of data most coaches and sports analysts do not use or use very little information supplied by sports statistics companies.
There have been a number of patents and patent applications around trying to solve this problem and problems like it.
Orenstein in U.S. Pat. No. 6,270,433 discloses a system for automatically determining the occurrence of off side which involves detection of players relative to one another and the ball in the confines of a soccer field. This system is specifically focused around using player and object tracking technology to determine referee associated events such as the game object leaving the playing area and off side infractions.
They are not associated to determining player or team performance abilities.
Daver in U.S. Pat. No. 5,513,854 describes a system and process for acquiring and processing position and/or physical performance data of one or more persons and a ball on a game field. The system discloses a system for capturing in real time, the instantaneous position of multiple players and a ball using transmitters on the ball and individual players. Together with digital imaging of the ball and players, it creates a set of digital values that describes the performances of every player based on their position and trajectory data over time. The patent also discloses the display of values and statistics on an interface.
While Daver determines the performances of every player, this does not use preconfigured automatic classifications to determine performance and is focused on physical performances rather than skills.
Min describes a system and process in WO 01/88826 for acquiring and processing position and/or physical performance of one or more persons on a game field. The system uses soccer game record data that is converted to location data for graphical display. Also disclosed is the selection of specific time periods by a user and the output of user defined formats such as name/number of the player passing or receiving the ball, type of pass, kind of event, goal success or fail and position of the player before shooting.
Min discloses post match analysis based on user selections of time periods and analysts types. Team sports ‘events’ are determined but this is post match and manual and does not use preconfigured automatic classifications to determine performance.
In WO 2008/033338, Aman describes a system and method for automatically determining the states of game object possession for sporting contests. The intended use is to determine possession and possession flow within sporting contests. This is achieved through the determination of location of each player and the game object such as a ball on the playing area such as a playing field, rink, or court. This together with determining the state of play within a specific area and within a specific radius of each player. Statistics are automatically presented concerning possession events related to the location of each player.
This system discloses determination of possession and statistics related to changes in game possession of the game object.
It does not automatically identify skill events based on preconfigured classifications of multiple location thresholds of multiple players and a game object like a ball or puck.
Seacat in US 2008/0140233 discloses a method and apparatus for analysing team effectiveness based on information from an ongoing sports game. The system automatically analyses team effectiveness by transmitting location data on at least some of the players to a computer which processes the data in real time.
This system is focused on team effectiveness rather than the skill classification of an individual player within a team.
In U.S. Pat. No. 7,580,912, Carlbon describes a performance data mining system combining sensor and other data to discover interesting patterns and rules for performance. The system processes data captured during and associated with an event, where data comprises motion data of one or more objects and people to discover one or more attributes of the objects or people that are associated with the outcome of the event. The attributes are not predetermined to be associated with the outcome of the event and these attributes comprise style, strategy and performance. This patent is focused on discovering the relationship between attributes and outcomes rather than classifying skills based on pre-determined parameters.
House describes in US 2010/0030350 a system for generating and analysing information for an athletic event. The system gathers data from multiple objects including players and game objects such as balls or pucks together with aspect data for objects such as location and motion and determines a data representation with respect to the aspects of the different objects.
This system is focused on the relationship between aspects of multiple objects to create statistics rather than classifying skills of an individual player based on predetermined parameters.
The prior art does not use pre-determined classifications based on location pattern geo spatial groupings/relationships of multiple players and the game object such as a ball or puck.
Further the state of the art does not refer to the automatic determination through predetermined classifications of an individual team position or players skills with respect to the sports game being analysed.
Automated Classifications of various player skills within the team dependent on a game situation save the coach, trainer or sports analyst the effort of working through the processing of large amounts of data manually thus reducing time and effort.
Automated skill classification not only saves time but also meaningful analysis can be conducted in real time during a game or practice.
Automated skill classification augments coaching significantly. Coaching is primarily concerned with identifying the biggest performance problems for a player and setting ‘training’ to improve them. The higher the degree of accuracy in diagnosing performance issues, the more individualised the future prescribed training activities with respect to the player. This results in greater and more rapid the improvement in game ability.
Coaches prefer to deal with issues that are ‘real’. They prefer to take game events where possession, field position, tactical advantage or points were lost or gained and find out why they occurred. Classifications where each of these positive and negative situations is compartmentalised with all the relevant data for more intense analysis as mentioned above is key to interpreting the many possible issues that may occur in a game.
If a game event occurs, ideally all data relevant to the situation is captured, analysed and interpreted to aid the coach.
While previous systems can be used to define assists, passes and goals for example, they are unable to automatically characterise the quality of the assist, pass or goal. The point of this classification system is to look at team and player skills at a far more detailed level than the previous state of the art
Without detailed identification of various game events like goals, assists, passes and tackles, statistics are produced but these statistics do not adequately characterise the quality of a players skills.
Without a clear and detailed understanding of the quality of a player's skills, it is difficult to make judgements on tactical scenarios real time in-game and it is also very difficult to provide adequate coaching feedback to players for training to promote performance improvement.
It is an object of the present invention to provide a method and system for enhancing classification and interpretation of activities that occur in team sports games by combining multiple game parameters most particularly distance that a player is from the ball and from other players and their location on the field to provide a clearer determination of the physical, physiological, technical and tactical aspects of team sports, or to at least provide the public with a useful choice.